Determining Lane Specific Speed Information

ABSTRACT

A method is presented which comprises:
         obtaining or holding available map data representing, at least in part, a travel network comprising a plurality of links,   determining at least one lane specific speed profile for a specific lane of at least one selected link of said plurality of links at least partially based on perpendicular distances between said selected link as represented by said map data and positions represented by a plurality of probe data points.       

     Further presented are inter-alia corresponding apparatuses, a corresponding system and a corresponding computer program code.

FIELD

The invention relates to the field of determining speed information andmore specifically to determining speed information (e.g. one or morespeed profiles) for a vehicle (e.g. for autonomous vehicles,highly-assisted-driving vehicles and/or vehicles with predictive cruisecontrol).

BACKGROUND

There have been multiple recent developments in the transportationtechnology that are revolutionizing the way people experience driving.Such technologies include connected vehicles with mobile access to theinternet, adaptive cruise control or autonomous navigation andplatooning. Adaptive cruise control systems automatically change thevehicle speed to accommodate curves, traffic congestions or roadincidents. Autonomous vehicles take a step farther by taking control notonly of the vehicle speed but also of the wheel steering, when turningor changing lanes, with the ultimate goal of taking full control of thewhole driving process and thus enabling drivers to become passengers,with all the benefits associated with being relieved from the task ofdriving. Finally, platooning of multiple trucks would save energy,reduce CO2 emissions and reduce the strain of human drivers. Platooningcould open new avenues not only in the trucking industry but also forconsumers by releasing the drivers from the task of maneuvering thevehicles. In such platoons the lead vehicle drives either manually orautonomously, followed by vehicles without autonomous driving capacity,in which the drivers become passengers.

SUMMARY OF SOME EMBODIMENTS OF THE INVENTION

In all of the three cases described above, adaptive cruise control,autonomous navigation and platooning, it is conceivable that thevehicles would benefit from using the collective knowledge accumulatedfrom other traffic participants such as human and/or robotic drivingexperiences. An important quantity that defines the motion of a vehicleis the speed. Traffic participants travel at different speeds on a givenlink such as a road section. The distribution of these speeds on thelink may be characterized by one or more statistical distributiondescriptors, including, but not limited to, mean speed, median speed,other speed percentiles, and various measures of the distribution widthand shape. The evolution of one or more of these descriptors along alink may be understood to be represented by a speed profile. Forexample, a median speed profile may represent one or more speeds alongthe link such that half of the vehicles travel slower, and the otherhalf travel at higher speeds. The speed profiles may be created withhigh spatial granularity along the link. Such speed profiles can be usedby predictive cruise control and autonomous vehicles to provide ahuman-like driving experience.

According to an exemplary aspect of the invention, a method ispresented, which comprises:

-   -   obtaining or holding available map data representing, at least        in part, a travel network comprising a plurality of links,    -   determining at least one lane specific speed profile for a        specific lane of at least one selected link of the plurality of        links at least partially based on perpendicular distances        between the selected link as represented by the map data and        positions represented by a plurality of probe data points.

The presented method may be performed by an apparatus or by a pluralityof apparatuses. For example, the presented method may be performed byany one embodiment of the below presented apparatuses. Alternatively oradditionally, the presented method may be performed by a plurality ofany one embodiment of the below presented apparatuses.

Holding available the map data may be understood to mean that the mapdata are stored in memory means of an apparatus performing the method.Example of memory means include a volatile memory and a non-volatilememory. Alternatively or additionally, the map data could be obtained bydetermining the map data or by receiving the map data, for example by acommunication interface of an apparatus performing the presented method.

The map data may represent the travel network at least in part byrepresenting a map of at least a part of the travel network.

A link may be a section of the travel network, for example a sectionbetween two junctions of the travel network. A link may be a directionallink (i.e. only relating to one travel direction on the link) or anon-directional link (relating to more than one travel direction on thelink). Each link may be associated with a link identifier (e.g. a uniquelink identifier). The at least one selected link could be such a link ofthe travel network, for example an arbitrary link of the travel networkwhich is selected for performing the method.

A lane may be understood to be a part of a link of the travel networkthat is designated for use by a single line of traffic participantsand/or vehicles travelling in the same direction. A link may comprise aplurality of lanes. For example, the selected link may comprise at leasttwo lanes and the specific lane for which the lane specific speedprofile is to be determined may be one lane of these at least two lanesof the selected link. The lanes of a link may be numbered. For example,the lanes of a directional link may be numbered from the left to theright in ascending order and by an increment of 1. In this example, theleftmost lane may have the number one and the number of the rightmostlane may correspond to the total number of lanes.

A lane specific speed profile may represent speed information, forexample speed information for a vehicle. Examples for speed informationare a speed, an acceleration, a deceleration or a combination thereof. Alane specific speed profile may be a speed profile for a specific laneof the selected link representing speed information for all vehiclestravelling on this specific lane of the selected link.

A probe data point may be understood to represent travel information(e.g. historic travel information) of a mobile device such as a vehicle,a navigation device and/or a smartphone. For example, a probe data pointmay represent a position captured by the mobile device and, optionally,further travel information (e.g. a speed) captured by the mobile deviceat this position. For example, the position may be a Global NavigationSatellite System (GNSS) position captured by a GNSS sensor of the mobiledevice.

For determining a lane specific speed profile for the selected link onlyprobe data points may be used that represent a position that is on theselected link or matchable with the selected link as represented by themap data. A position may be understood to be matchable with the selectedlink, if it is matched with a position on the selected lane by a mapmatching algorithm. For example, a simple map matching algorithm wouldbe projecting a probe point to the closest link. More sophisticatedalgorithms combine information on speed, direction of travel, anduncertainties in the input quantities.

For example, each probe data point of the plurality of probe data pointsrepresents a position that is on the selected link or matchable with theselected link as represented by the map data. The plurality of probedata points may comprise at least two probe data points, morespecifically at least three probe data points. For example, theplurality of probe data points may comprise more than 100 probe datapoints.

As disclosed in more detail below, each perpendicular distance of theperpendicular distances may describe the shortest distance between theselected link (e.g. the center or a side of the specific link) asrepresented by the map data and a position represented by a probe datapoint of the plurality of probe data points. By statistical analysis, astrong dependence of the lane on which a mobile device traveled when itcaptured a position and the perpendicular distance can be shown. Thisdependency is used when determining the at least one lane specific speedprofile at least partially based on the perpendicular distances betweenthe selected link as represented by the map data and positionsrepresented by a plurality of probe data points.

This may have the effect that lane specific speed profile may bedetermined even if no lane specific map data exist.

Determining the at least one lane specific speed profile at leastpartially based on the perpendicular distances may be understood to meanthat the at least one lane specific speed profile is determined at leastpartially as a function of the perpendicular distances. For example,determining the at least one lane specific speed profile at leastpartially based on the perpendicular distances may mean that theperpendicular distances are determined and/or used for determining theat least one lane specific speed profile. Alternatively or additionally,it may mean that a function and/or a relationship which at leastpartially depends on the perpendicular distances is determined, modeledand/or used for determining the at least one lane specific speedprofile.

The presented method may be for determining lane specific speedinformation, for example for determining lane specific speed informationfor a vehicle (e.g. a vehicle travelling on the specific lance of theselected link).

According to a further exemplary aspect of the invention, an apparatusis presented, which comprises means for performing, at least in part,the steps of any one embodiment of the presented method.

The means of the presented apparatus can be implemented in hardwareand/or software. They may comprise for instance a processor forexecuting computer program code for realizing the required functions, amemory storing the program code, or both. Alternatively, they couldcomprise for instance circuitry that is designed to realize the requiredfunctions, for instance implemented in a chipset or a chip, like anintegrated circuit. The presented apparatus may comprise a single meansfor all functions, a common plurality of means for all functions, or aplurality of different means for different functions.

According to a further exemplary aspect of the invention, anotherapparatus is presented, which comprises at least one processor and atleast one memory including computer program code, the at least onememory and the computer program code with the at least one processorconfigured to cause the apparatus at least to perform, at least in part,the steps of any one embodiment of the presented method.

The presented apparatuses may be modules or components for a device, forexample chips. Alternatively, the presented apparatuses may be devices.The presented apparatuses may comprise only the disclosed components(e.g. means) or may further comprise one or more additional components.

The presented apparatuses may be for determining lane specific speedinformation, for example for determining lane specific speed informationfor a vehicle (e.g. a vehicle travelling on the specific lance of theselected link).

The presented apparatuses may be or may be part of one of a server, astationary device, a module for a device, a vehicle or an embeddednavigation device of a vehicle.

According to a further exemplary aspect of the invention, a system ispresented which comprises a plurality of apparatuses which areconfigured to perform, together, the steps of any one embodiment of thepresented method. The apparatuses of the presented system may at leastpartially correspond to any one of the presented apparatuses accordingto an exemplary aspect of the invention. For example, the presentedsystem may comprise a server or a server cloud for determining speedinformation for a vehicle and a vehicle.

According to a further exemplary aspect of the invention, anon-transitory computer readable storage medium is presented, in whichcomputer program code is stored. The non-transitory computer readablestorage medium may be tangible. The computer program code causes atleast one apparatus to perform the steps of any one embodiment of thepresented method when executed by a processor. The computer program codecould be stored in the computer readable storage medium in the form ofinstructions encoding the computer-readable storage medium. The computerreadable storage medium may be intended for taking part in the operationof a device, like an internal or external hard disk of a computer, or beintended for distribution of the program code, like an optical disc.

According to a further exemplary aspect of the invention, a computerprogram code is presented, the computer program code when executed by aprocessor causing an apparatus to perform the steps of any oneembodiment of the presented method.

In the following, further features and embodiments of these exemplaryaspects of the invention will be described.

According to an exemplary embodiment of the invention, the travelnetwork is a road network. Accordingly, a link of the travel networkrepresented by the map data may be understood to correspond to a roadsection of the road network, for example a road section between twojunctions of the road network; and a lane of a link of the travelnetwork represented by the map data may be understood to corresponds toa road lane of a road section of the road network.

According to an exemplary embodiment of the invention, the presentedmethod further comprises obtaining or holding available the plurality ofprobe data points.

Holding available the plurality of probe data points may be understoodto mean that the plurality of probe data points is stored in memorymeans of an apparatus performing the method. Alternatively oradditionally, the plurality of probe data points could be obtained byreceiving the plurality of probe data points, for example bycommunication means of an apparatus performing the presented method.

According to an exemplary embodiment of the invention, the plurality ofprobe data points are part of one or more probe data sets of a pluralityof probe data sets.

A probe data set may comprise one or more probe data points.

For example, each probe data set of the plurality of probe data sets maycomprise a sequence of probe data points captured by a mobile device(e.g. a vehicle, a navigation device and/or a smartphone). This may havethe effect that a specific probe data set of the plurality of probe datasets and the probe data points of this specific probe data set of theplurality of probe data sets may be associated with a travel route of aspecific mobile device. For example, the probe data points of thespecific probe data set may represent positions and speeds captured bythe specific mobile device when travelling along this travel route.

For example, each probe data point of a specific probe data set maycomprise the identifier of the probe data set. The identifier may beindicative for a session of a service provider collecting probe datasets of a mobile device, a mobile device, or a combination thereof.

Each of these probe data sets may thus be considered to representhistoric travel experiences of human drivers travelling along thistravel route. A considerable amount of such probe data sets has beencollected by service providers over time and this plurality of(historic) probe data sets can be used for determining lane specificspeed profiles. For example, the plurality of probe data sets maycomprise at least two probe data sets, more specifically at least threeprobe data sets. For example, the plurality of probe data sets maycomprise more than 1000 probe data sets. To avoid a falsification of thespeed profiles due to traffic congestions, only probe data setscollected during weekends and during night, from 8 pm to 7 am, duringbusiness days may be used for determining lane specific speed profilesin certain exemplary embodiments of the invention.

For determining a lane specific speed profile for the selected link onlyprobe data sets may be used that are associated with the selected link.A probe data set may be understood to be associated with the selectedlink if the probe data set comprises at least one probe data pointrepresenting a position that is on the selected link or matchable withthe selected link as represented by the map data.

This may have the effect that a speed profile for the selected link isdetermined based on one or more probe data points representing one ormore speeds and positions of specific mobile devices which weretravelling along a travel route including the selected link.

According to an exemplary embodiment of the invention, each probe datapoint of the plurality of probe data points represents a position and aspeed, for example a position of a mobile device and a speed of thismobile device at this position.

As disclosed above, examples of such a mobile device may be a vehicle, anavigation device and/or a smartphone.

As further disclosed above, the position may be a position captured bythe mobile device. For example, the position may be a Global NavigationSatellite System (GNSS) position captured by a GNSS sensor of the mobiledevice. For example, in determining a lane specific speed profile forthe selected link only probe data points may be used that represent aposition that is on the selected link or matchable with the selectedlink as represented by the map data.

The speed may be a speed captured by the mobile device. Alternatively oradditionally, the speed may be determined at least partially based onone or more positions captured by the mobile device, for example bycalculating the speed based on the distance between two positions (e.g.subsequently) captured by the mobile device and the time differencebetween capturing these two positions.

According to an exemplary embodiment of the invention, the lane specificspeed profile for the selected link represents one speed (e.g. one speedvalue) for the specific lane of the selected link or a plurality ofspeeds (e.g. a plurality of speed values) for a plurality of subsequentsegments of the specific lane of the selected link.

Each of the plurality of speeds may be a speed for a respective segmentof the plurality of subsequent segments of the specific lane of selectedlink. For example, the selected link may be divided into the pluralityof subsequent segments. This may enable a high spatial granularity ofthe lane specific speed profile. Each of the segments may have the samelength (e.g. 10 m). Alternatively the segments may have at leastpartially different length.

As disclosed above, for determining a lane specific speed profile forthe selected link only probe data points may be used that represent aposition that is on the selected link or matchable with the selectedlink as represented by the map data. This may be particularlyadvantageous, if the lane specific speed profile for the selected linkrepresents one speed for the specific lane of the selected link.

However, if the lane specific speed profile for the selected linkrepresents a plurality of speeds for a plurality of subsequent segmentsof the specific lane of the selected link, it may be useful to furtherdivide the probe data points based on the subsequent segments. In thiscase, for each segment of the plurality of segments only probe datapoints may be used for determining a lane specific speed profile for theselected link that represent a position that is on the respectivesegment of the plurality of subsequent segments or matchable with therespective segment of the plurality of subsequent segments asrepresented by the map data.

According to an exemplary embodiment of the invention, eachperpendicular distance of the perpendicular distances represents theshortest distance between a position represented by a respective probedata point of the plurality of probe data points and the selected linkof the travel network as represented by the map data.

The selected link may be represented by the map data as a line segment,for example as a line segment at the center or a side of the selectedlink. Accordingly, each perpendicular distance of the perpendiculardistances may represent the shortest distance between a positionrepresented by a respective probe data point of the plurality of probedata points and the line segment represented by the map data.

For example, the lane specific speed profile is at least partiallydetermined by determining a linear regression for modeling arelationship between the speeds represented by the plurality of probedata points and the perpendicular distances and/or by determining aslope of the linear regression.

In certain exemplary embodiments of the invention, the lane specificspeed profile may at least partially determined by determining a lanespecific speed deviation at least partially based on the slope of thelinear regression. For example, the lane specific speed deviation may beat least partially determined based on a total number of lanes of theselected link, a width of the specific lane of the selected link, anumber of the specific lane or a combination thereof. For example, thespeed deviation ΔV may be determined as follows:

${\Delta \; v} = {{S \cdot W}{\frac{N - {2L} + 1}{2}.}}$

In this example, S corresponds to the slope of the linear regression, Wcorresponds to the width of the specific lane, N corresponds to thetotal number of lanes of the selected link, and L corresponds to thenumber of the specific lane.

Preferably, the width of the lanes of the selected link may be equal.However, it is also possible that the width of the lanes of the selectedlink is at least partially different.

As disclosed above, the lanes of the selected link may be numbered. Forexample, the lanes of the selected link may be numbered from the left tothe right in ascending order and by an increment of 1 if the selectedlink is a directional link. The number of the specific lane and thedifference between the number of the specific lane and the total numberof lanes of the selected link may thus be indicative for the position ofthe specific lane on the selected link.

In case the lane specific speed profile represents a plurality of speedsfor a plurality of subsequent segments of the selected link, for eachsegment of the subsequent segments, a linear regression and/or a lanespecific speed deviation may be determined based on probe data pointsrepresenting a position on the respective segment.

According to an exemplary embodiment of the invention, the lane specificspeed profile is at least partially determined by determining at leastone of a mean speed, a median speed and a speed percentile at leastpartially based on the speeds represented by the plurality of probe datapoints.

Alternatively or additionally, the at least one of a mean speed, amedian speed and a speed percentile may be determined in advance and,for example, corresponding speed information may be stored in memorymeans of an apparatus performing the method. In this case, the lanespecific speed profile may be at least partially determined by using orobtaining at least one of a mean speed, a median speed and a speedpercentile that were determined at least partially based on the speedsrepresented by the plurality of probe data points.

Examples of determined speed percentiles are a 10% speed percentile, a25% speed percentile, a 30% speed percentile, a 50% speed percentile(i.e. the median speed), a 70% speed percentile, a 75% speed percentile,a 90% speed percentile or combinations thereof.

In case the lane specific speed profile represents a plurality of speedsfor a plurality of subsequent segments of the selected link, for eachsegment of the subsequent segments, a mean speed or a speed percentilemay be determined based on probe data points representing a position onthe respective segment.

For example, different speed percentiles may be used for lane specificspeed profiles for vehicles transporting human passengers (which have toaccount for the passenger comfort) and for vehicles transporting onlygoods or traveling empty to pick up passengers. In the latter case,without the constraints related to the human comfort, it is conceivablethat autonomous vehicles would drive much faster than the ones driven byhumans, while still maintaining the highest safety standards. For speedprofiles for vehicles transporting human passengers for example the 50%speed percentile (i.e. the median speed) or the 70% speed percentile maybe used; and for speed profiles for vehicles transporting only goods ortraveling empty to pick up passengers for example the 75% speedpercentile or the 90% speed percentile may be used.

According to an exemplary embodiment of the invention, the lane specificspeed profile is determined by determining a sum of the lane specificspeed deviation and the at least one of a mean speed, a median speed anda speed percentile.

In case the lane specific speed profile represents a plurality of speedsfor a plurality of subsequent segments of the selected link, for eachsegment of the subsequent segments, a sum of a lane specific speeddeviation and the at least one of a mean speed, a median speed and aspeed percentile determined for the respective segment may bedetermined.

According to an exemplary embodiment of the invention, the presentedmethod further comprises

-   -   providing or generating speed map data representing an        association of the map data and a plurality of lane specific        speed profiles for links of the travel network, the plurality of        lane specific speed profiles comprising the at least one lane        specific speed profile determined for the specific lane of the        selected link.

According to an exemplary embodiment of the invention, the presentedmethod further comprises:

-   -   determining speed information for at least one vehicle;    -   providing the speed information to the at least one vehicle.

For example, the at least one vehicle may request speed information fora travel route from an apparatus performing the presented method (e.g. aserver, e.g. a server of the presented system). The request may specifythe links (e.g. by link identifiers) and lanes on the links (e.g. bylane numbers) included in the travel route.

The travel route may have been determined by the vehicle based on mapdata obtained or hold available by the at least one vehicle.Alternatively or additionally, the travel route may have been determinedby a server (e.g. a server of the presented system) for the vehicle.Determining the travel route information may be at least partially basedon a starting position and a destination position. For example, thestarting position may represent the current position of the vehicle andthe destination position may represent a position of a desireddestination of a user of the vehicle. The starting position and thedestination position may be obtained by the at least one vehicle, forexample by capturing the current position of the vehicle by a GNSSsensor and/or by receiving a user input on a user interface of thevehicle.

The speed information for the at least one vehicle may be determined torepresent the lane specific speed profiles for the lanes on the linksthat are included in the travel route. For example, the speedinformation may represent the lane specific speed profile determined forthe specific lane of the selected link.

Providing the speed information to the at least one vehicle may beunderstood to mean that the speed information are communicated or causedto be communicated to the at least one vehicle by an apparatusperforming the presented method (e.g. by a server, e.g. a server of thepresented system), for example by a communication interface of theapparatus. Alternatively or additionally, the speed information may beprovided for use by the at least one vehicle, for example, for, at leastin part, autonomously driving along the travel route. The speedinformation may cause the at least one vehicle to at least partiallycontrol and/or adapt its speed accordingly when driving along the routerepresented by the travel route information.

For example, the at least one vehicle may be, at least in part, anautonomous vehicle, a highly-assisted-driving vehicle, a vehicle withpredictive cruise control, or a combination thereof. In certainexemplary embodiments of the invention, the vehicle may determine thespeed information. Alternatively or additionally, the speed informationmay be determined by a server and communicated by the server to thevehicle.

According to an exemplary embodiment of the invention, the presentedmethod is performed for a plurality of links of the travel network asselected link. This is understood to mean that the steps of thepresented method may be performed (e.g. repeated) for further links ofthe plurality of links of the travel network as selected link.

It is to be understood that the presentation of the invention in thissection is merely by way of examples and non-limiting.

Other features of the invention will become apparent from the followingdetailed description considered in conjunction with the accompanyingdrawings. It is to be understood, however, that the drawings aredesigned solely for purposes of illustration and not as a definition ofthe limits of the invention, for which reference should be made to theappended claims. It should be further understood that the drawings arenot drawn to scale and that they are merely intended to conceptuallyillustrate the structures and procedures described herein.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1a is a block diagram of an exemplary embodiment of a systemaccording to the invention;

FIG. 1b is a block diagram of an exemplary embodiment of an apparatusaccording to the invention;

FIGS. 2a-b are flowcharts of exemplary embodiments methods according tothe invention;

FIGS. 3a-c are illustrations of at least a part of an exemplary travelnetwork represented by map data and exemplary probe data sets ofspecific mobile devices travelling in the travel network;

FIG. 4 is an exemplary plot showing a relationship between speeds andperpendicular distances; and

FIG. 5 is a schematic illustration of examples of tangible andnon-transitory storage media according to the present invention.

The following description serves to deepen the understanding of thepresent invention and shall be understood to complement and be readtogether with the description of example embodiments of the invention asprovided in the above SUMMARY section of this specification.

By way of example, considering an autonomous vehicle driving in freeflow conditions, the autonomous vehicle equipped with high performancesensors and sufficient computing power continuously scans theenvironment and decides, in real time, the optimal driving speed, takinginto consideration multiple road factors such as speed limit, slope,curvature, number of lanes or lane widths. As these factors change alongthe road, the vehicle may continuously adapt the speed to a value whichis optimal from the points of view of both safety and, if humans aretransported by the vehicle, driver/passenger comfort. While the vehiclemay be able to perform such computations in real time and to adopt theoptimal speed based on sensor data only, such computations may be aidedby “prior” knowledge for example provided by travel route informationrepresenting the current travel route of the vehicle and speedinformation representing speed profiles for the current travel route ofthe vehicle. For example, the vehicle could be configured to adapt thevehicle speed by default according to the speed information, while thefinal decision regarding the speed would be taken based on theinformation collected by the real time sensors. Since the vehicle may bealready in the most probable state according to the historicalexperience of other drivers encoded in the speed profile, the real timechanges dictated by the sensor observations would be minimal and onlyneeded when deviations from the historical norm are observed. Suchdeviations may be expected in many cases which include recurring trafficcongestion associated with the normal business hours and non-recurringcongestion due to adverse weather conditions or incidents. Thedifference between an autonomous vehicle driving with and without thisprior knowledge may be thought as analogous to the difference between ahuman driving in a familiar or un-familiar area. The same as the humandriver is required to focus harder to understand the surroundings andnavigate in an un-familiar area, the same way, an autonomous vehicle isexpected to require more sensor information and computational power whendriving without any prior knowledge.

Autonomous vehicles and vehicles equipped with predictive control canthus benefit from having available speed profiles or speed map datawhich provides typical driving speeds on every link with high spatialgranularity, in particular lane specific speed profiles or speed mapdata which provide typical driving speeds on every lane of every link.Such lane specific speed profiles can be used by predictive cruisecontrol and autonomous vehicles to provide a human-like drivingexperience.

FIG. 1a is a schematic high-level block diagram of a system 10 accordingto an exemplary aspect of the invention. System 10 comprises a server11, which may alternatively be embodied as a server cloud (e.g. aplurality of servers connected e.g. via the internet and providingservices at least partially jointly), and a vehicle 12.

According to exemplary embodiments of the present invention, server 11may determine speed information for vehicle 12.

Vehicle 12 may be, at least in part, an autonomous vehicle, ahighly-assisted-driving vehicle, a vehicle with predictive cruisecontrol, or a combination thereof.

Communication between server 11 and vehicle 12 may for example takeplace at least partially in a wireless fashion, e.g. based on cellularradio communication or on Wireless Local Area Network (WLAN) basedcommunication, to name but a few examples. In this way, mobility andconnectivity of vehicle 12 is guaranteed.

FIG. 1b is a block diagram of an apparatus 100 according to an exemplaryaspect of the invention. Apparatus 100 may for example represent server11 of system 10. Alternatively or additionally, apparatus 100 may forexample represent an embedded navigational device of vehicle 12 ofsystem 10.

Apparatus 100 comprises a processor 101. Processor 101 may represent asingle processor or two or more processors, which are for instance atleast partially coupled, for instance via a bus. Processor 101 executesa program code stored in program memory 102 (for instance program codecausing apparatus 100 to perform one or more of the embodiments of amethod (or parts thereof) according to the invention (as for instancefurther described below with reference to FIGS. 2a-b ), when executed onprocessor 101), and interfaces with a main memory 103. Some or all ofmemories 102 and 103 may also be included into processor 101. One of orboth of memories 102 and 103 may be fixedly connected to processor 101or at least partially removable from processor 101, for instance in theform of a memory card or stick. Program memory 102 may for instance be anon-volatile memory. It may for instance be a FLASH memory (or a partthereof), any of a ROM, PROM, EPROM, MRAM or a FeRAM (or a part thereof)or a hard disc (or a part thereof), to name but a few examples. Programmemory 102 may also comprise an operating system for processor 101.Program memory 102 may for instance comprise a first memory portion thatis fixedly installed in apparatus 100, and a second memory portion thatis removable from apparatus 100, for instance in the form of a removableSD memory card.

Main memory 103 may for instance be a volatile memory. It may forinstance be a DRAM memory, to give non-limiting example. It may forinstance be used as a working memory for processor 101 when executing anoperating system and/or programs.

Processor 101 further controls an optional communication interface 104configured to communicate with other devices (e.g. with server 11 orvehicle 12), for example by receiving and/or sending data and/orinformation. The communication may for example be based on a wirelesscommunication connection. The communication interface 104 may thuscomprise circuitry such as modulators, filters, mixers, switches and/orone or more antennas to allow wireless transmission and/or reception ofsignals. In embodiments of the invention, communication interface 104 isinter alia configured to allow communication based on a cellular radiocommunication (e.g. a 2G/3G/4G/5G cellular radio communication) and/or anon-cellular radio communication (e.g. a WLAN or Bluetoothcommunication). The 2G/3G/4G/5G cellular radio communication standardsare developed by the 3GPP and presently available underhttp://www.3gpp.org/. The Bluetooth standards are specified by theBluetooth Special Interest Group and is presently available underhttps://www.bluetooth.com/. WLAN is for examply specified by thestandards of the IEEE 802.11 family (cf. http://www.ieee.org/).Alternatively or additionally, the communication may equally well bebased on a wirebound communication connection or a combination ofwireless and wirebound communication connections. Accordingly, thecommunication interface 104 may thus comprise circuitry such asmodulators, filters, mixers, switches to allow a wirebound transmissionand/or reception of signals. In embodiments of the invention,communication interface 104 is inter alia configured to allowcommunication based on an Ethernet communication such as a LAN (LocalArea Network) communication.

Processor 101 further controls an optional user interface 105 configuredto present information to a user of apparatus 100 and/or to receiveinformation from such a user. User interface 105 may for instance be thestandard user interface via which a user of apparatus 100 controls otherfunctionality thereof. Examples of such a user interface are atouch-sensitive display, a keyboard, a touchpad, a display, etc.

The components 102-105 of apparatus 100 may for instance be connectedwith processor 101 by means of one or more serial and/or parallelbusses.

It is to be understood that apparatus 100 may comprise various othercomponents (e.g. a positioning sensor such as a Global NavigationSatellite System (GNSS) sensor).

FIG. 2a is a flowchart 200 illustrating an exemplary embodiment of amethod according to the invention. In the following, it is assumed thatthe steps of this flowchart 200 are performed by server 11 of system 10of FIG. 1 a.

In a step 201, server 11 obtains or holds available map datarepresenting, at least in part, a travel network comprising a pluralityof links. The map data may for example be received by communicationinterface 104 of server 11 and, subsequently, be stored in memory 102 ofserver 11. For example, the map data may be part of a navigationdatabase stored in memory 102 of server 11.

FIG. 3a illustrates at least in part an exemplary travel network 300 arepresented by such map data. Travel network 300 a comprises links 301,302, 303, 304 and 305 and junctions 306 and 307. A link of travelnetwork 300 a may for example be understood to represent a section ofthe travel network which is between two junctions like link 301 which isbetween junctions 306 and 307.

In the following, it is assumed that the reference signs of the linkscorrespond to unique link identifiers associated with the links and atravel direction of the links may be indicated by the characters “T” or“F”. Thus, link 301 may be identified by “301T” in the travel directionindicated by arrow A in FIG. 3a and by “301F” in the other traveldirection.

As shown in FIG. 3b , link 301T may comprise three lanes 301T-1, 301T-2and 301T-3 which are numbered from the left to the right in ascendingorder. Thus, lane number 1 of link 301T corresponds to lane 301T-1, lanenumber 2 of link 301T corresponds to lane 301T-2 and lane number 3 oflink 301T corresponds to lane 301T-3. The lane width W may be equal foreach of the lanes 301T-1, 301T-2 and 301T-3.

In the following, it is assumed that the map data is not lane specificand only defines a line segment B as a representation of link 301T.Therein, line segment B represents the center of link 301T as shown inFIG. 3 b.

In a step 202, server 11 determines at least one lane specific speedprofile for a specific lane of at least one selected link of theplurality of links at least partially based on perpendicular distancesbetween the selected link as represented by the map data and positionsrepresented by a plurality of probe data points.

The plurality of probe data points may be stored in program memory 102of server 11.

The probe data points may be part of one or more probe data sets of aplurality of probe data sets. Therein, each probe data set of theplurality of probe data sets may comprise a sequence of probe datapoints representing a position and a speed of a mobile device. Eachprobe data point of a specific probe data set may comprise theidentifier of the probe data set. The identifier may be indicative for asession of a service provider collecting probe data sets of a mobiledevice, a mobile device, or a combination thereof. Examples of such amobile device may be a vehicle (e.g. vehicle 12), a navigation deviceand/or a smartphone.

The positions represented by the sequence of probe data points of arespective probe data set of the plurality of probe data sets may beGNSS positions captured by a GNSS sensor of a specific mobile devicewhen travelling along a travel route. A considerable amount of suchprobe data sets has been collected by service providers over time andthis plurality of (historic) probe data sets can be used for determininglane specific speed profiles for links of a travel network. To avoid afalsification of the lane specific speed profiles due to trafficcongestions, only probe data sets collected during weekends and duringnight, from 8 pm to 7 am, during business days may be used fordetermining lane specific speed profiles.

For determining a lane specific speed profile for the selected link onlyprobe data sets may be used that are associated with the selected link.For example, the plurality of probe data points may comprise the probedata points of the probe data sets that are determined to be associatedwith the selected link. Furthermore, the plurality of probe data pointsmay only comprise probe data points that are determined to represent aposition that is on the selected link or matchable with the selectedlink.

A probe data set may be understood to be associated with the selectedlink if the probe data set comprises at least one probe data pointrepresenting a position that is on the selected link or matchable withthe selected link as represented by the map data. The determiningwhether one or more probe data sets of a plurality of probe data setsare associated with at least one selected link of the travel networkbased on one or more positions represented by one or more probe datapoints of the one or more probe data sets may be performed in anoptional step prior to step 202. As a result of such a determining, alist with identifiers of probe data sets that have been determined to beassociated with the selected link may be obtained. Alternatively oradditionally, the probe data sets associated with the selected link ofthe travel network may be determined in advance and, for example,corresponding association information (e.g. a list with identifiers ofprobe data sets that have been determined to be associated with theselected link) may be stored in program memory 102 of server 11.

For example, the determining whether a probe data point represent aposition that is on the selected link or matchable with the selectedlink may be only performed for the probe data points of the probe datasets that are determined to be associated with the selected link. Thisdetermining whether a probe data point represent a position that is onthe selected link or matchable with the selected link may be performedin step 202.

In the following it is assumed that link 301T is the selected link andthe plurality of probe data points represent positions that arematchable with selected link 301T (e.g. matchable by a map matchingalgorithm).

FIG. 3c illustrates exemplary probe data sets 308 and 309 of specificmobile devices travelling in the travel network 300 a of FIGS. 3a -c.

The positions represented by the sequence of probe data points of probedata sets 308 and 309 are indicated by points in FIG. 3c . For example,probe data set 308 comprises a sequence of probe data points includingprobe data points representing positions 310 and 311; and probe data set309 comprises a sequence of probe data points including probe datapoints representing positions 312 and 313.

Probe data set 308 may have been generated by a mobile device (e.g. avehicle) travelling on lane 301T-1 of selected link 301T, and probe dataset 309 may have been generated by a mobile device (e.g. a vehicle)travelling on lane 301T-3 of selected link 301T. In the following, it isassumed that the positions represented by the sequence of probe datapoints of probe data sets 308 and 309 shown in FIG. 3c are matchable(i.e. can be matched) with link 301T which is represented by the mapdata as line segment B by a map matching algorithm.

Since both probe data sets 308 and 309 comprise probe data pointsrepresenting one or more positions that are matchable with link 301T,they may be determined to be associated with selected link 301T. Thelist with identifiers of probe data sets that have been determined to beassociated with the selected link 301T may be thus as follows: {308,309, . . . }.

A lane specific speed profile for the selected link 301T may representspeed information (e.g. one or more speeds) for a specific lane of lanes301T-1, 301T-2 and 301T-3 of selected link 301T. In the following it isassumed that a lane specific speed profile for the selected link 301Trepresents one speed for the selected link 301T.

Since the map data is not lane specific and only defines a line segmentB which is arranged at the center of link 301T as a representation oflink 301T, it is not possible to directly create a lane specific speedprofile for a specific lane of the selected link 301T. However,statistical analysis shows strong speed dependence on perpendiculardistance from the center of the link. This dependency is exemplary shownby the plot 400 of FIG. 4 and can be used for determining a lanespecific speed profile.

In FIG. 4, the Y-axis 401 corresponds to speeds represented by theplurality of probe data points (e.g. representing a position that ismatchable with a selected link, e.g. link 301T). The X-axis 402corresponds to the perpendicular distance between the selected link(e.g. link 301T) as represented by the map data and positionsrepresented by the plurality of probe data points. In FIG. 4, each pointrepresents a relationship between an average speed represented byvehicles on that link from all lanes. The regression line shows thetendency of speed away from the center of the road. The vertical line oneach point represent the error of mean and horizontal line on the pointthe width of a bin on the x-axis. The vertical errors get increasedbeyond ±10 meters which illustrates the approximate road width might be20 meters. The reason there are still points beyond 10 meters is due toGPS position errors.

As disclosed above, a perpendicular distance may be understood torepresent the shortest distance between a position represented by arespective probe data point and the selected link of the travel networkas represented by the map data. In FIG. 3c exemplary perpendiculardistances Δd between line segment B (i.e. link 301T as represented bythe map data) and positions represented by the probe data points ofprobe data sets 308 and 309 are shown as shortest distances. For probedata points representing a position on the left side of line segment B(e.g. probe data point 310), the perpendicular distance Δd is negative;and for probe data points representing a position on the right side ofline segment B (e.g. probe data point 312), the perpendicular distanceΔd is positive.

In FIG. 4, error bars are used to indicate the uncertainty of the speedsand positions represented by the probe data points. These uncertaintiesmay be for example due to position capturing inaccuracies and poorposition resolution. For example, the positions represented by the probedata points may be beside the link (cf. position represented by probedata point 313 in FIG. 3c ). Accordingly, the perpendicular distancesmay not reflect the real link width.

As apparent from FIG. 4, probe data points representing a position onthe left side of the selected link (e.g. link 301T) significantlyrepresent higher speeds than probe data points representing a positionon the right side of the selected link (e.g. link 301T). From thisobservation, it may be concluded that the historic speed of vehiclestravelling on the leftmost lane of the selected link was typicallyhigher than the speed of vehicles travelling on the rightmost lane ofthe selected link. Despite the above described uncertainties, therelationship between the speeds and perpendicular distances of theplurality of probe data points can thus be used for determining a lanespecific speed profile for a specific lane of the selected link (e.g.link 301T) in step 202. For example, this relationship can be modeled bylinear regression 403 as shown in FIG. 4.

Accordingly, the lane specific speed profile may be at least partiallydetermined by determining a linear regression for modeling arelationship between the speeds represented by the plurality of probedata points and the perpendicular distances and/or by determining aslope of the linear regression in step 202.

In step 202, the lane specific speed profile may be determined bydetermining a lane specific speed deviation and at least one of a meanspeed, a speed percentile and a median speed based on the plurality ofprobe data points. Alternatively or additionally, the at least one of amean speed, a median speed and a speed percentile may be determined inadvance and, for example, corresponding speed information may be storedin program memory 102 of server 11 (e.g. as part of the map data).

The speed represented by the lane specific speed profile for thespecific lane of the selected link (e.g. link 301T) may then bedetermined by determining the sum of the lane specific speed deviationand the at least one of a mean speed, a median speed and a speedpercentile as follows:

v=v _(m) +Δv.

Therein, v corresponds to the speed that is to be represented by thelane specific speed profile for the specific lane of the selected link,v_(m) corresponds to the at least one of a mean speed, a median speedand a speed percentile, and Δv corresponds to the lane specific speeddeviation.

The lane specific speed deviation Δv may be determined as follows:

${\Delta \; v} = {{S \cdot W}{\frac{N - {2L} + 1}{2}.}}$

Therein, S corresponds to the slope of the linear regression (e.g.linear regression 403) modeling a relationship between the speedsrepresented by the plurality of probe data points and the perpendiculardistances, W corresponds to the width of the specific lane, Ncorresponds to the total number of lanes of the selected link, and Lcorresponds to the number of the specific link. In case the lanespecific speed profile is for lane 301T-1 of selected link 301T, N wouldbe 1 (i.e. the number of lane 301T-1) and L would be 3 (i.e. the totalnumber of lanes of selected link 301T).

The steps 201 to 202 may be repeated for each lane of each link of thetravel network represented by the map data. Subsequently, a speed mapdata may be generated representing an association of the map data and aplurality of lane specific speed profiles for the links of the travelnetwork.

FIG. 2b is a flowchart 210 illustrating optional steps of the methodaccording to the invention. The steps of flowchart 210 may for examplebe performed subsequent to the steps of flowchart 200 of FIG. 2a . Inthe following, it is assumed that the steps of this flowchart 210 areperformed by server 11 of system 10 of FIG. 1a . Alternatively oradditionally, the steps of this flowchart may be at least partiallyperformed by vehicle 12 of system 10 of FIG. 1 a.

In step 211, server 11 determines speed information for vehicle 12. Forexample, the speed information may at least partially represent the atleast one lane specific speed profile for a specific lane of the atleast one selected link determined in step 202 of flowchart 200 of FIG.2 a.

For example, server 11 may receive a request for determining speedinformation for a travel route from vehicle 12. The request may specifythe links and for each link one or more lanes included in the travelroute.

The speed information for vehicle 12 may be determined to represent thelane specific speed profiles for the links and lanes of the links thatare included in the travel route.

By way of example, the speed information may represent lane specificspeed profiles at which despite the automotive capabilities of vehicle12 in terms of speed, acceleration, braking, etc., a user may still feelcomfortable and safe within the vehicle while traveling and/or thevehicle may interact safely with one or more other vehicles on the sameroute.

In step 212, server 11 may provide the speed information to vehicle 12.For example the speed information may be communicated from server 11 tovehicle 12.

Vehicle 12 may use the speed information for, at least in part,autonomously driving along the travel route. The speed information maycause vehicle 12 to at least partially control and/or adapt its speedaccordingly when driving along the route represented by the travel routeinformation.

FIG. 5 is a schematic illustration of examples of tangible andnon-transitory computer-readable storage media according to the presentinvention that may for instance be used to implement program memory 102of FIG. 1. To this end, FIG. 5 displays a flash memory 500, which mayfor instance be soldered or bonded to a printed circuit board, asolid-state drive 501 comprising a plurality of memory chips (e.g. Flashmemory chips), a magnetic hard drive 502, a Secure Digital (SD) card503, a Universal Serial Bus (USB) memory stick 504, an optical storagemedium 505 (such as for instance a CD-ROM or DVD) and a magnetic storagemedium 506.

In the present specification, any presented connection in the describedembodiments is to be understood in a way that the involved componentsare operationally coupled. Thus, the connections can be direct orindirect with any number or combination of intervening elements, andthere may be merely a functional relationship between the components.

Further, as used in this text, the term ‘circuitry’ refers to any of thefollowing: (a) hardware-only circuit implementations (such asimplementations in only analog and/or digital circuitry) (b)combinations of circuits and software (and/or firmware), such as: (i) toa combination of processor(s) or (ii) to portions ofprocessor(s)/software (including digital signal processor(s)), software,and memory(ies) that work together to cause an apparatus, such as amobile phone, to perform various functions) and (c) to circuits, such asa microprocessor(s) or a portion of a microprocessor(s), that requiresoftware or firmware for operation, even if the software or firmware isnot physically present.

This definition of ‘circuitry’ applies to all uses of this term in thistext, including in any claims. As a further example, as used in thistext, the term ‘circuitry’ also covers an implementation of merely aprocessor (or multiple processors) or portion of a processor and its (ortheir) accompanying software and/or firmware. The term ‘circuitry’ alsocovers, for example, a baseband integrated circuit or applicationsprocessor integrated circuit for a mobile phone.

Any of the processors mentioned in this text, in particular but notlimited to processor 101 of FIG. 1b , could be a processor of anysuitable type. Any processor may comprise but is not limited to one ormore microprocessors, one or more processor(s) with accompanying digitalsignal processor(s), one or more processor(s) without accompanyingdigital signal processor(s), one or more special-purpose computer chips,one or more field-programmable gate arrays (FPGAS), one or morecontrollers, one or more application-specific integrated circuits(ASICS), or one or more computer(s). The relevant structure/hardware hasbeen programmed in such a way to carry out the described function.

Moreover, any of the methods, processes, steps and actions described orillustrated herein may be implemented using executable instructions in ageneral-purpose or special-purpose processor and stored on acomputer-readable storage medium (e.g., disk, memory, or the like) to beexecuted by such a processor. References to a ‘computer-readable storagemedium’ should be understood to encompass specialized circuits such asFPGAs, ASICs, signal processing devices, and other devices.

The expression “A and/or B” is considered to comprise any one of thefollowing three scenarios: (i) A, (ii) B, (iii) A and B. Furthermore,the article “a” is not to be understood as “one”, i.e. use of theexpression “an element” does not preclude that also further elements arepresent. The term “comprising” is to be understood in an open sense,i.e. in a way that an object that “comprises an element A” may alsocomprise further elements in addition to element A.

It will be understood that all presented embodiments are only exemplary,and that any feature presented for a particular example embodiment maybe used with any aspect of the invention on its own or in combinationwith any feature presented for the same or another particular exampleembodiment and/or in combination with any other feature not mentioned.In particular, the example embodiments presented in this specificationshall also be understood to be disclosed in all possible combinationswith each other, as far as it is technically reasonable and the exampleembodiments are not alternatives with respect to each other. It willfurther be understood that any feature presented for an exampleembodiment in a particular category (method/apparatus/computer program)may also be used in a corresponding manner in an example embodiment ofany other category. It should also be understood that presence of afeature in the presented example embodiments shall not necessarily meanthat this feature forms an essential feature of the invention and cannotbe omitted or substituted.

The sequence of all method steps presented above is not mandatory, alsoalternative sequences may be possible. Nevertheless, the specificsequence of method steps exemplarily shown in the figures shall beconsidered as one possible sequence of method steps for the respectiveembodiment described by the respective figure.

The invention has been described above by means of example embodiments.It should be noted that there are alternative ways and variations whichare obvious to a skilled person in the art and can be implementedwithout deviating from the scope of the appended claims.

1. A method, said method comprising: obtaining or holding available mapdata representing, at least in part, a travel network comprising aplurality of links, determining at least one lane specific speed profilefor a specific lane of at least one selected link of said plurality oflinks at least partially based on perpendicular distances between saidselected link as represented by said map data and positions representedby a plurality of probe data points.
 2. The method according to claim 1,said method further comprising: obtaining or holding available saidplurality of probe data points, wherein said plurality of probe datapoints are of one or more probe data sets of a plurality of probe datasets, wherein said one or more probe data sets are associated with saidselected link.
 3. The method according to claim 1, wherein said selectedlink is represented by said map data as a line segment.
 4. The methodaccording claim 3, wherein each probe data point of said plurality ofprobe data points represents a position associated with said selectedlink.
 5. The method according to claim 4 wherein said lane specificspeed profile is at least partially determined by determining a linearregression for modeling a relationship between said speeds representedby said plurality of probe data points and said perpendicular distancesor by determining a slope of said linear regression or by a combinationthereof.
 6. The method according to claim 5, wherein said lane specificspeed profile is at least partially determined by determining a lanespecific speed deviation at least partially based on said slope of saidlinear regression.
 7. The method according to claim 6, wherein said lanespecific speed profile is at least partially determined by determiningat least one of a mean speed, a median speed and a speed percentile atleast partially based on said speeds represented by said plurality ofprobe data points.
 8. The method according to claim 7, wherein said lanespecific speed profile is at least partially determined by determining asum of said lane specific speed deviation and said at least one of amean speed, a median speed and a speed percentile.
 9. An apparatuscomprising at least one processor and at least one memory includingcomputer program code for one or more programs, said at least one memoryand said computer program code configured to, with said at least oneprocessor, cause said apparatus to: obtain or hold available map datarepresenting, at least in part, a travel network comprising a pluralityof links, determine at least one lane specific speed profile for aspecific lane of at least one selected link of said plurality of linksat least partially based on perpendicular distances between saidselected link as represented by said map data and positions representedby a plurality of probe data points.
 10. The apparatus according toclaim 9, wherein said at least one memory and said computer program codeare configured to, with said at least one processor, further cause theapparatus to: obtain or hold available said plurality of probe datapoints, wherein said plurality of probe data points are of one or moreprobe data sets of a plurality of probe data sets, wherein said one ormore probe data sets are associated with said selected link.
 11. Theapparatus according to claim 9, wherein said lane specific speed profilefor said selected link represents one speed for said specific lane ofsaid selected link or a plurality of speeds for said specific lane ofsaid selected link for a plurality of subsequent segments of saidselected link.
 12. The apparatus according to claim 9, wherein eachperpendicular distance of said perpendicular distances represents theshortest distance between a position represented by a respective probedata point of said plurality of probe data points and said selected linkof said travel network as represented by said map data.
 13. Theapparatus according to claim 9, wherein said selected link isrepresented by said map data as a line segment.
 14. The apparatusaccording to claim 9, wherein each probe data point of said plurality ofprobe data points represents a position and a speed.
 15. The apparatusaccording to claim 14, wherein each probe data point of said pluralityof probe data points represents a position associated with said selectedlink.
 16. The apparatus according to claim 15, wherein said lanespecific speed profile is at least partially determined by determining alinear regression for modeling a relationship between said speedsrepresented by said plurality of probe data points and saidperpendicular distances or by determining a slope of said linearregression or by a combination thereof.
 17. The apparatus according toclaim 16, wherein said lane specific speed profile is at least partiallydetermined by determining a lane specific speed deviation at leastpartially based on said slope of said linear regression.
 18. Theapparatus according to claim 17, wherein said lane specific speedprofile is at least partially determined by determining at least one ofa mean speed, a median speed and a speed percentile at least partiallybased on said speeds represented by said plurality of probe data points.19. The apparatus according to claim 9, wherein said lane specific speedprofile is at least partially determined by determining a sum of saidlane specific speed deviation and said at least one of a mean speed, amedian speed and a speed percentile.
 20. The apparatus according toclaim 9, wherein said lane specific speed profile is at least partiallydetermined based on a total number of lanes of said selected link, awidth of a lane of said selected link, a number of said specific lane ora combination thereof.
 21. The apparatus according to claim 9, whereinsaid at least one memory and said computer program code are configuredto, with said at least one processor, further cause the apparatus to:provide or generate speed map data representing an association of saidmap data and a plurality of lane specific speed profiles for links ofsaid travel network, said plurality of lane specific speed profilescomprising said at least one lane specific speed profile determined forsaid specific lane of said selected link.
 22. The apparatus according toclaim 9, wherein said at least one memory and said computer program codeare configured to, with said at least one processor, further cause theapparatus to: determine speed information for at least one vehicle;provide said speed information to said at least one vehicle.
 23. Theapparatus according to claim 9, wherein said apparatus is one of: aserver; a stationary device; a module for a device; a vehicle; anembedded navigation device of a vehicle.
 24. A non-transitory computerreadable storage medium including one or more sequences of one or moreinstructions which, when executed by one or more processors, cause anapparatus to: obtain or hold available map data representing, at leastin part, a travel network comprising a plurality of links, determine atleast one lane specific speed profile for a specific lane of at leastone selected link of said plurality of links at least partially based onperpendicular distances between said selected link as represented bysaid map data and positions represented by a plurality of probe datapoints.